Predictors of low diabetes risk perception in a multi‐ethnic cohort of women with gestational diabetes mellitus
Bibliographic record
Abstract
AIM: To determine what proportion of women with gestational diabetes underestimate their diabetes risk and identify factors associated with low diabetes risk perception. METHODS: Participants included pregnant adult women with gestational diabetes between 2009 and 2012 across seven diabetes clinics in Ontario, Canada. Data were collected through chart review and a survey that included a diabetes risk perception question. RESULTS: Of the 614 of 902 women (68% response rate) with gestational diabetes, 89% correctly responded that gestational diabetes increases the risk for developing diabetes. However, 47.1% of women perceived themselves to be at low risk for developing diabetes within 10 years. On multivariable analysis, BMI < 25 kg/m(2) , absent previous gestational diabetes history, absent diabetes family history and absent insulin use were appropriately associated with low diabetes risk perception. However, compared with Caucasian ethnicity, high-risk ethnicity (Aboriginal, Latin American, West Indian, South Asian, Middle Eastern, Filipino, Black, Pacific Islander) [odds ratio (OR) 2.07; 95% CI 1.30-3.31] and East and South East Asian ethnicity (OR 2.01; 1.10-3.67) were associated with low diabetes risk perception. After further adjustment for immigration, only high-risk ethnicity remained a predictor of low diabetes risk perception (OR 1.86; 1.09-3.19), whereas East and South East Asian ethnicity did not (OR 1.67; 0.86-3.22). CONCLUSIONS: Although the majority of women recognized gestational diabetes as a risk factor for diabetes, almost half underestimated their personal high diabetes risk despite prenatal care. Furthermore, women from high-risk ethnic groups were more likely to underestimate their risk, even after adjusting for immigration. Interventions tailored to these groups are necessary to enhance perceived diabetes risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".